code: def get_wordlm():
      n_layers = 1
      n_units = 8
  
      char_list = ["<blank>", "<space>", "a", "b", "c", "d", "<eos>"]
      word_list = ["<blank>", "<unk>", "ab", "id", "ac", "bd", "<eos>"]
  
      char_dict = {x: i for i, x in enumerate(char_list)}
      word_dict = {x: i for i, x in enumerate(word_list)}
  
      word_rnnlm = lm_pytorch.ClassifierWithState(
          lm_pytorch.RNNLM(len(word_list), n_layers, n_units)
      )
      word_rnnlm = lm_pytorch.ClassifierWithState(
          extlm_pytorch.LookAheadWordLM(
              word_rnnlm.predictor, word_dict, char_dict
          )
      )
  
      return word_rnnlm
  
imports: import argparse
  import json
  import tempfile
  import pytest
  import torch
  from packaging.version import parse
  import espnet.lm.pytorch_backend.extlm
  import espnet.nets.pytorch_backend.lm.default
  from espnet.asr.pytorch_backend.asr_init import load_trained_model
  from espnet.nets.beam_search_transducer import BeamSearchTransducer
  from espnet.nets.pytorch_backend.e2e_asr_transducer import E2E
  from espnet.nets.pytorch_backend.transducer.blocks import build_blocks
label_desc: Use image node_id 5 for calling a global function with example usage: get_wordlm() and returns: word_rnnlm